[CCCL] 瘦身 + 补全: 移除 cudax/python/libcudacxx-tests 冗余文件, 新增 c2h 测试助手 + cmake 构建系统 + 8 个 CUDA thrust examples

变更摘要:
- 删除: cudax/ (783 files, 7.2M) — 实验性组件，竞赛不需要
- 删除: python/ (226 files, 2.0M) — Python 绑定，竞赛不需要
- 删除: libcudacxx/{test,benchmarks,codegen,cmake,share} (4432 files, 31M)
  保留: libcudacxx/include/ (1463 headers, cuda::std 编译依赖)
- 新增: c2h/ (27 files) — CUB Catch2 测试辅助头文件，编译 243 个测试必需
- 新增: cmake/ (29 files) — CCCL 原生 CMake 构建系统
- 新增: thrust/examples/cuda/ (7 files) + cpp_integration/ (1 file)
  async_reduce, custom_temporary_allocation, explicit_cuda_stream,
  global_device_vector, range_view, unwrap_pointer, wrap_pointer, device

结果: cccl_upstream 从 74M→35M (瘦身 53%), 核心内容 100% 保留:
  27/27 tuning headers, 78 benchmarks, 243 tests,
  60 thrust examples, 18 CUB examples, 全部编译头文件

cccl_upstream/cudax/examples/stf/launch_scan.cu

@@ -1,162 +0,0 @@
-//===----------------------------------------------------------------------===//
-//
-// Part of CUDASTF in CUDA C++ Core Libraries,
-// under the Apache License v2.0 with LLVM Exceptions.
-// See https://llvm.org/LICENSE.txt for license information.
-// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
-// SPDX-FileCopyrightText: Copyright (c) 2022-2024 NVIDIA CORPORATION & AFFILIATES.
-//
-//===----------------------------------------------------------------------===//
-
-/**
- * @file
- *
- * @brief A parallel scan algorithm
- *
- */
-
-#include <cub/cub.cuh> // or equivalently <cub/device/device_scan.cuh>
-
-#include <cuda/experimental/stf.cuh>
-
-using namespace cuda::experimental::stf;
-
-__host__ __device__ double X0(int)
-{
-  //    return sin((double) i);
-  return 1.0;
-}
-
-int main(int argc, char** argv)
-{
-  stream_ctx ctx;
-  // graph_ctx ctx;
-
-  size_t N = 128 * 1024UL * 1024UL;
-  if (argc > 1)
-  {
-    N = size_t(atoll(argv[1]));
-  }
-
-  int check = 0;
-  if (argc > 2)
-  {
-    check = atoi(argv[2]);
-  }
-
-  std::vector<double> X(N);
-
-  for (size_t i = 0; i < N; i++)
-  {
-    X[i] = X0(i);
-  }
-
-  auto lX = ctx.logical_data(&X[0], N);
-
-  // No need to move this back to the host if we do not check the result
-  if (!check)
-  {
-    lX.set_write_back(false);
-  }
-
-  cuda_safe_call(cudaStreamSynchronize(ctx.fence()));
-
-  cudaEvent_t start, stop;
-  cuda_safe_call(cudaEventCreate(&start));
-  cuda_safe_call(cudaEventCreate(&stop));
-  cuda_safe_call(cudaEventRecord(start, ctx.fence()));
-
-  constexpr size_t BLOCK_THREADS = 128;
-  constexpr size_t NBLOCKS       = 8;
-
-  auto spec = con<NBLOCKS>(con<BLOCK_THREADS>(), mem(NBLOCKS * sizeof(double)));
-
-  // auto where = exec_place::repeat(exec_place::current_device(), NDEVS);
-  auto where = exec_place::current_device();
-
-  ctx.launch(spec, where, lX.rw())->*[=] _CCCL_DEVICE(auto th, auto x) {
-    const size_t block_id = th.rank(0);
-    const size_t tid      = th.inner().rank();
-    // const size_t tid = th.rank(1, 0);
-
-    // Block-wide partials using static allocation
-    __shared__ double block_partial_sum[th.static_width(1)];
-
-    // Device-wide partial sums
-    slice<double> dev_partial_sum = th.template storage<double>(0);
-
-    /* Thread local prefix-sum */
-    const box<1> b = th.apply_partition(shape(x), std::tuple<blocked_partition, blocked_partition>());
-    for (size_t i = b.get_begin(0) + 1; i < b.get_end(0); i++)
-    {
-      x(i) += x(i - 1);
-    }
-    block_partial_sum[tid] = x(b.get_end(0) - 1);
-
-    th.inner().sync();
-
-    /* Block level : get partials sum across the different threads */
-    if (tid == 0)
-    { // rank in scope block is 0
-      // Prefix sum on partial sums
-      for (size_t i = 1; i < BLOCK_THREADS; i++)
-      {
-        block_partial_sum[i] += block_partial_sum[i - 1];
-      }
-      dev_partial_sum[block_id] = block_partial_sum[BLOCK_THREADS - 1];
-    }
-
-    /* Reduce partial sums at device level : get sum across all blocks */
-    th.sync();
-
-    if (block_id == 0 && tid == 0)
-    { // rank in scope 0
-      for (size_t i = 1; i < NBLOCKS; i++)
-      {
-        dev_partial_sum[i] += dev_partial_sum[i - 1];
-        //  printf("SUMMED dev_partial_sum[%ld] = %f\n", i, dev_partial_sum[i]);
-      }
-    }
-
-    th.sync();
-
-    for (size_t i = b.get_begin(0); i < b.get_end(0); i++)
-    {
-      if (tid > 0)
-      {
-        x(i) += block_partial_sum[tid - 1];
-      }
-
-      if (block_id > 0)
-      {
-        x(i) += dev_partial_sum[block_id - 1];
-      }
-    }
-  };
-
-  cuda_safe_call(cudaEventRecord(stop, ctx.fence()));
-
-  ctx.finalize();
-
-  float ms = 0;
-  cuda_safe_call(cudaEventElapsedTime(&ms, start, stop));
-
-  printf("%s in %f ms (%g GB/s)\n",
-         pretty_print_bytes(N * sizeof(double)).c_str(),
-         ms,
-         double(N * sizeof(double) / 1024 / 1024) / ms);
-
-  if (check)
-  {
-    fprintf(stderr, "Checking result...\n");
-    EXPECT(fabs(X[0] - X0(0)) < 0.00001);
-    for (size_t i = 0; i < N; i++)
-    {
-      if (fabs(X[i] - X[i - 1] - X0(i)) > 0.00001)
-      {
-        fprintf(stderr, "I %zu X[i] %f (X[i] - X[i-1]) %f expect %f\n", i, X[i], (X[i] - X[i - 1]), X0(i));
-      }
-      EXPECT(fabs(X[i] - X[i - 1] - X0(i)) < 0.00001);
-    }
-  }
-}